Overview

Dataset statistics

Number of variables19
Number of observations144984
Missing cells136579
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 MiB
Average record size in memory152.0 B

Variable types

TimeSeries15
Numeric4

Alerts

PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB) is highly overall correlated with PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB) and 5 other fieldsHigh correlation
PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB) is highly overall correlated with PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB) and 8 other fieldsHigh correlation
PRESSAO ATMOSFERICA MAX.NA HORA ANT. (AUT)(mB) is highly overall correlated with PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB) and 5 other fieldsHigh correlation
PRESSAO ATMOSFERICA MIN. NA HORA ANT. (AUT)(mB) is highly overall correlated with PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB) and 5 other fieldsHigh correlation
RADIACAO GLOBAL(Kj/m²) is highly overall correlated with TEMPERATURA DO AR - BULBO SECO - HORARIA(°C) and 3 other fieldsHigh correlation
TEMPERATURA DO AR - BULBO SECO - HORARIA(°C) is highly overall correlated with PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB) and 6 other fieldsHigh correlation
TEMPERATURA DO PONTO DE ORVALHO(°C) is highly overall correlated with PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB) and 5 other fieldsHigh correlation
TEMPERATURA MAXIMA NA HORA ANT. (AUT)(°C) is highly overall correlated with PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB) and 6 other fieldsHigh correlation
TEMPERATURA MINIMA NA HORA ANT. (AUT)(°C) is highly overall correlated with PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB) and 5 other fieldsHigh correlation
TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT)(°C) is highly overall correlated with PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB) and 5 other fieldsHigh correlation
TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT)(°C) is highly overall correlated with PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB) and 5 other fieldsHigh correlation
UMIDADE REL. MAX. NA HORA ANT. (AUT)(%) is highly overall correlated with TEMPERATURA DO AR - BULBO SECO - HORARIA(°C) and 4 other fieldsHigh correlation
UMIDADE REL. MIN. NA HORA ANT. (AUT)(%) is highly overall correlated with RADIACAO GLOBAL(Kj/m²) and 5 other fieldsHigh correlation
UMIDADE RELATIVA DO AR - HORARIA(%) is highly overall correlated with RADIACAO GLOBAL(Kj/m²) and 5 other fieldsHigh correlation
VENTO - RAJADA MAXIMA(m/s) is highly overall correlated with VENTO - VELOCIDADE HORARIA(m/s)High correlation
VENTO - VELOCIDADE HORARIA(m/s) is highly overall correlated with VENTO - RAJADA MAXIMA(m/s)High correlation
PRECIPITACAO TOTAL - HORARIO(mm) has 7203 (5.0%) missing valuesMissing
PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB) has 7044 (4.9%) missing valuesMissing
PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB) has 10674 (7.4%) missing valuesMissing
PRESSAO ATMOSFERICA MAX.NA HORA ANT. (AUT)(mB) has 7458 (5.1%) missing valuesMissing
PRESSAO ATMOSFERICA MIN. NA HORA ANT. (AUT)(mB) has 7458 (5.1%) missing valuesMissing
RADIACAO GLOBAL(Kj/m²) has 7087 (4.9%) missing valuesMissing
TEMPERATURA DO AR - BULBO SECO - HORARIA(°C) has 7044 (4.9%) missing valuesMissing
TEMPERATURA DO PONTO DE ORVALHO(°C) has 7273 (5.0%) missing valuesMissing
TEMPERATURA MAXIMA NA HORA ANT. (AUT)(°C) has 7458 (5.1%) missing valuesMissing
TEMPERATURA MINIMA NA HORA ANT. (AUT)(°C) has 7458 (5.1%) missing valuesMissing
TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT)(°C) has 7700 (5.3%) missing valuesMissing
TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT)(°C) has 7700 (5.3%) missing valuesMissing
UMIDADE REL. MAX. NA HORA ANT. (AUT)(%) has 7698 (5.3%) missing valuesMissing
UMIDADE REL. MIN. NA HORA ANT. (AUT)(%) has 7698 (5.3%) missing valuesMissing
UMIDADE RELATIVA DO AR - HORARIA(%) has 7272 (5.0%) missing valuesMissing
VENTO - DIRECAO HORARIA (gr)(° (gr)) has 7430 (5.1%) missing valuesMissing
VENTO - RAJADA MAXIMA(m/s) has 7494 (5.2%) missing valuesMissing
VENTO - VELOCIDADE HORARIA(m/s) has 7430 (5.1%) missing valuesMissing
time is non stationaryNon stationary
PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB) is non stationaryNon stationary
PRESSAO ATMOSFERICA MAX.NA HORA ANT. (AUT)(mB) is non stationaryNon stationary
PRESSAO ATMOSFERICA MIN. NA HORA ANT. (AUT)(mB) is non stationaryNon stationary
RADIACAO GLOBAL(Kj/m²) is non stationaryNon stationary
TEMPERATURA DO AR - BULBO SECO - HORARIA(°C) is non stationaryNon stationary
TEMPERATURA DO PONTO DE ORVALHO(°C) is non stationaryNon stationary
TEMPERATURA MAXIMA NA HORA ANT. (AUT)(°C) is non stationaryNon stationary
TEMPERATURA MINIMA NA HORA ANT. (AUT)(°C) is non stationaryNon stationary
TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT)(°C) is non stationaryNon stationary
TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT)(°C) is non stationaryNon stationary
UMIDADE REL. MAX. NA HORA ANT. (AUT)(%) is non stationaryNon stationary
UMIDADE REL. MIN. NA HORA ANT. (AUT)(%) is non stationaryNon stationary
UMIDADE RELATIVA DO AR - HORARIA(%) is non stationaryNon stationary
VENTO - RAJADA MAXIMA(m/s) is non stationaryNon stationary
PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB) is seasonalSeasonal
PRESSAO ATMOSFERICA MAX.NA HORA ANT. (AUT)(mB) is seasonalSeasonal
PRESSAO ATMOSFERICA MIN. NA HORA ANT. (AUT)(mB) is seasonalSeasonal
RADIACAO GLOBAL(Kj/m²) is seasonalSeasonal
TEMPERATURA DO AR - BULBO SECO - HORARIA(°C) is seasonalSeasonal
TEMPERATURA DO PONTO DE ORVALHO(°C) is seasonalSeasonal
TEMPERATURA MAXIMA NA HORA ANT. (AUT)(°C) is seasonalSeasonal
TEMPERATURA MINIMA NA HORA ANT. (AUT)(°C) is seasonalSeasonal
TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT)(°C) is seasonalSeasonal
TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT)(°C) is seasonalSeasonal
UMIDADE REL. MAX. NA HORA ANT. (AUT)(%) is seasonalSeasonal
UMIDADE REL. MIN. NA HORA ANT. (AUT)(%) is seasonalSeasonal
UMIDADE RELATIVA DO AR - HORARIA(%) is seasonalSeasonal
VENTO - RAJADA MAXIMA(m/s) is seasonalSeasonal
PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB) is highly skewed (γ1 = 30.81578718)Skewed
time is uniformly distributedUniform
time has unique valuesUnique
PRECIPITACAO TOTAL - HORARIO(mm) has 126726 (87.4%) zerosZeros
VENTO - VELOCIDADE HORARIA(m/s) has 3582 (2.5%) zerosZeros

Reproduction

Analysis started2023-04-25 00:23:14.942331
Analysis finished2023-04-25 00:42:15.815064
Duration19 minutes and 0.87 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

time
Numeric time series

NON STATIONARY  UNIFORM  UNIQUE 

Distinct144984
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72491.5
Minimum0
Maximum144983
Zeros1
Zeros (%)< 0.1%
Memory size1.1 MiB
2023-04-25T00:42:15.972373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7249.15
Q136245.75
median72491.5
Q3108737.25
95-th percentile137733.85
Maximum144983
Range144983
Interquartile range (IQR)72491.5

Descriptive statistics

Standard deviation41853.42
Coefficient of variation (CV)0.57735624
Kurtosis-1.2
Mean72491.5
Median Absolute Deviation (MAD)36246
Skewness0
Sum1.0510108 × 1010
Variance1.7517088 × 109
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value1
2023-04-25T00:42:16.252210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
96659 1
 
< 0.1%
96653 1
 
< 0.1%
96654 1
 
< 0.1%
96655 1
 
< 0.1%
96656 1
 
< 0.1%
96657 1
 
< 0.1%
96658 1
 
< 0.1%
96660 1
 
< 0.1%
96651 1
 
< 0.1%
Other values (144974) 144974
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
144983 1
< 0.1%
144982 1
< 0.1%
144981 1
< 0.1%
144980 1
< 0.1%
144979 1
< 0.1%
144978 1
< 0.1%
144977 1
< 0.1%
144976 1
< 0.1%
144975 1
< 0.1%
144974 1
< 0.1%
2023-04-25T00:42:18.284489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

PRECIPITACAO TOTAL - HORARIO(mm)
Real number (ℝ)

MISSING  ZEROS 

Distinct162
Distinct (%)0.1%
Missing7203
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean0.17433028
Minimum0
Maximum74.8
Zeros126726
Zeros (%)87.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:18.977759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum74.8
Range74.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2942108
Coefficient of variation (CV)7.4239016
Kurtosis416.87459
Mean0.17433028
Median Absolute Deviation (MAD)0
Skewness16.262272
Sum24019.4
Variance1.6749816
MonotonicityNot monotonic
2023-04-25T00:42:19.451401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126726
87.4%
0.2 3653
 
2.5%
0.4 1179
 
0.8%
0.6 766
 
0.5%
0.8 615
 
0.4%
1 473
 
0.3%
1.2 402
 
0.3%
1.4 323
 
0.2%
1.6 273
 
0.2%
1.8 263
 
0.2%
Other values (152) 3108
 
2.1%
(Missing) 7203
 
5.0%
ValueCountFrequency (%)
0 126726
87.4%
0.2 3653
 
2.5%
0.4 1179
 
0.8%
0.6 766
 
0.5%
0.8 615
 
0.4%
1 473
 
0.3%
1.2 402
 
0.3%
1.4 323
 
0.2%
1.6 273
 
0.2%
1.8 263
 
0.2%
ValueCountFrequency (%)
74.8 1
< 0.1%
64.4 1
< 0.1%
53.4 1
< 0.1%
48.4 1
< 0.1%
46.4 1
< 0.1%
46.2 1
< 0.1%
44 1
< 0.1%
41.8 1
< 0.1%
41.4 1
< 0.1%
40.6 2
< 0.1%

PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct250
Distinct (%)0.2%
Missing7044
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean910.64416
Minimum897.7
Maximum923.3
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:19.807680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum897.7
5-th percentile905.4
Q1908.4
median910.5
Q3912.8
95-th percentile916.4
Maximum923.3
Range25.6
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation3.3339997
Coefficient of variation (CV)0.0036611443
Kurtosis-0.033160497
Mean910.64416
Median Absolute Deviation (MAD)2.2
Skewness0.13310272
Sum1.2561426 × 108
Variance11.115554
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.120642139 × 10-30
2023-04-25T00:42:20.249058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
910 1813
 
1.3%
909.7 1777
 
1.2%
909.8 1733
 
1.2%
910.7 1720
 
1.2%
909.1 1720
 
1.2%
910.8 1718
 
1.2%
910.1 1717
 
1.2%
910.5 1716
 
1.2%
909.3 1712
 
1.2%
909.5 1707
 
1.2%
Other values (240) 120607
83.2%
(Missing) 7044
 
4.9%
ValueCountFrequency (%)
897.7 1
 
< 0.1%
898.2 2
 
< 0.1%
898.3 1
 
< 0.1%
898.4 2
 
< 0.1%
898.5 3
< 0.1%
898.6 2
 
< 0.1%
898.8 2
 
< 0.1%
898.9 1
 
< 0.1%
899 5
< 0.1%
899.1 1
 
< 0.1%
ValueCountFrequency (%)
923.3 1
 
< 0.1%
923.1 1
 
< 0.1%
923 1
 
< 0.1%
922.9 2
< 0.1%
922.7 1
 
< 0.1%
922.6 1
 
< 0.1%
922.5 2
< 0.1%
922.4 4
< 0.1%
922.3 2
< 0.1%
922.2 2
< 0.1%
2023-04-25T00:42:22.745628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct39501
Distinct (%)29.4%
Missing10674
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean2094.3184
Minimum1005.3147
Maximum1033836
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:23.374987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1005.3147
5-th percentile1014.6715
Q11018.9362
median1021.9852
Q31025.7381
95-th percentile1031.2583
Maximum1033836
Range1032830.7
Interquartile range (IQR)6.801919

Descriptive statistics

Standard deviation33066.009
Coefficient of variation (CV)15.788435
Kurtosis947.6394
Mean2094.3184
Median Absolute Deviation (MAD)3.348224
Skewness30.815787
Sum2.812879 × 108
Variance1.0933609 × 109
MonotonicityNot monotonic
2023-04-25T00:42:23.641825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1020.65198 34
 
< 0.1%
1021.709499 30
 
< 0.1%
1021.924254 30
 
< 0.1%
1021.474958 29
 
< 0.1%
1021.824109 29
 
< 0.1%
1021.455942 29
 
< 0.1%
1021.011961 29
 
< 0.1%
1021.035474 28
 
< 0.1%
1020.117603 28
 
< 0.1%
1022.772414 28
 
< 0.1%
Other values (39491) 134016
92.4%
(Missing) 10674
 
7.4%
ValueCountFrequency (%)
1005.31467 1
< 0.1%
1005.324235 1
< 0.1%
1005.580757 1
< 0.1%
1005.732254 1
< 0.1%
1005.915183 1
< 0.1%
1005.958143 1
< 0.1%
1005.988213 1
< 0.1%
1006.098426 1
< 0.1%
1006.184298 1
< 0.1%
1006.209672 1
< 0.1%
ValueCountFrequency (%)
1033836 1
 
< 0.1%
1032127 1
 
< 0.1%
1030946 4
< 0.1%
1028503 3
 
< 0.1%
1027862 4
< 0.1%
1027728 8
< 0.1%
1025601 3
 
< 0.1%
1025543 7
< 0.1%
1025357 1
 
< 0.1%
1023888 3
 
< 0.1%

PRESSAO ATMOSFERICA MAX.NA HORA ANT. (AUT)(mB)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct247
Distinct (%)0.2%
Missing7458
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean910.88527
Minimum898.5
Maximum923.4
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:23.881398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum898.5
5-th percentile905.6
Q1908.6
median910.7
Q3913.1
95-th percentile916.6
Maximum923.4
Range24.9
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.3141856
Coefficient of variation (CV)0.0036384227
Kurtosis-0.025505015
Mean910.88527
Median Absolute Deviation (MAD)2.2
Skewness0.13866353
Sum1.2527041 × 108
Variance10.983826
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.700389956 × 10-30
2023-04-25T00:42:24.144124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
910 1830
 
1.3%
910.8 1812
 
1.2%
910.1 1759
 
1.2%
910.3 1759
 
1.2%
909.7 1755
 
1.2%
910.5 1736
 
1.2%
910.6 1725
 
1.2%
910.2 1708
 
1.2%
909.8 1693
 
1.2%
911 1693
 
1.2%
Other values (237) 120056
82.8%
(Missing) 7458
 
5.1%
ValueCountFrequency (%)
898.5 4
< 0.1%
898.6 2
< 0.1%
898.8 3
< 0.1%
898.9 1
 
< 0.1%
899 1
 
< 0.1%
899.1 2
< 0.1%
899.2 2
< 0.1%
899.3 1
 
< 0.1%
899.4 2
< 0.1%
899.5 4
< 0.1%
ValueCountFrequency (%)
923.4 2
 
< 0.1%
923.3 1
 
< 0.1%
923.2 1
 
< 0.1%
923 1
 
< 0.1%
922.9 2
 
< 0.1%
922.7 3
< 0.1%
922.6 1
 
< 0.1%
922.5 3
< 0.1%
922.4 5
< 0.1%
922.3 5
< 0.1%
2023-04-25T00:42:25.418648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

PRESSAO ATMOSFERICA MIN. NA HORA ANT. (AUT)(mB)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct250
Distinct (%)0.2%
Missing7458
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean910.39172
Minimum897.7
Maximum923
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:25.809283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum897.7
5-th percentile905.1
Q1908.1
median910.2
Q3912.6
95-th percentile916.2
Maximum923
Range25.3
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.3446049
Coefficient of variation (CV)0.0036738085
Kurtosis-0.039719515
Mean910.39172
Median Absolute Deviation (MAD)2.2
Skewness0.12997203
Sum1.2520253 × 108
Variance11.186382
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.173749338 × 10-30
2023-04-25T00:42:26.084927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
909.7 1812
 
1.2%
910 1779
 
1.2%
909.4 1747
 
1.2%
909.1 1728
 
1.2%
909.9 1709
 
1.2%
909.8 1705
 
1.2%
909 1703
 
1.2%
909.3 1694
 
1.2%
909.6 1688
 
1.2%
909.5 1682
 
1.2%
Other values (240) 120279
83.0%
(Missing) 7458
 
5.1%
ValueCountFrequency (%)
897.7 2
< 0.1%
898 2
< 0.1%
898.1 2
< 0.1%
898.2 1
 
< 0.1%
898.3 1
 
< 0.1%
898.4 2
< 0.1%
898.5 3
< 0.1%
898.6 2
< 0.1%
898.7 2
< 0.1%
898.8 4
< 0.1%
ValueCountFrequency (%)
923 1
 
< 0.1%
922.9 2
 
< 0.1%
922.7 1
 
< 0.1%
922.5 1
 
< 0.1%
922.4 3
< 0.1%
922.3 1
 
< 0.1%
922.2 2
 
< 0.1%
922.1 1
 
< 0.1%
922 5
< 0.1%
921.9 5
< 0.1%
2023-04-25T00:42:27.318812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

RADIACAO GLOBAL(Kj/m²)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct59361
Distinct (%)43.0%
Missing7087
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean491100.41
Minimum-3538
Maximum4385403
Zeros1209
Zeros (%)0.8%
Memory size1.1 MiB
2023-04-25T00:42:27.711238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-3538
5-th percentile-3266
Q1-3.54
median294
Q3500765
95-th percentile2724692
Maximum4385403
Range4388941
Interquartile range (IQR)500768.54

Descriptive statistics

Standard deviation924445.1
Coefficient of variation (CV)1.8823953
Kurtosis2.2527852
Mean491100.41
Median Absolute Deviation (MAD)2170
Skewness1.8557674
Sum6.7721273 × 1010
Variance8.5459873 × 1011
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.048347114 × 10-25
2023-04-25T00:42:27.991415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.54 21463
 
14.8%
-4 9990
 
6.9%
-3 4038
 
2.8%
-2 2204
 
1.5%
-1 1382
 
1.0%
0 1209
 
0.8%
-3538 700
 
0.5%
-3536 382
 
0.3%
-0.12 262
 
0.2%
-3535 248
 
0.2%
Other values (59351) 96019
66.2%
(Missing) 7087
 
4.9%
ValueCountFrequency (%)
-3538 700
0.5%
-3536 382
0.3%
-3535 248
 
0.2%
-3533 223
 
0.2%
-3531 167
 
0.1%
-3529 163
 
0.1%
-3528 155
 
0.1%
-3526 137
 
0.1%
-3524 106
 
0.1%
-3523 3
 
< 0.1%
ValueCountFrequency (%)
4385403 1
< 0.1%
4376379 1
< 0.1%
4322411 1
< 0.1%
4258305 1
< 0.1%
4250522 1
< 0.1%
4237973 1
< 0.1%
4232222 1
< 0.1%
4223379 1
< 0.1%
4218294 1
< 0.1%
4200431 1
< 0.1%
2023-04-25T00:42:29.285043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

TEMPERATURA DO AR - BULBO SECO - HORARIA(°C)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct334
Distinct (%)0.2%
Missing7044
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean19.746272
Minimum1.6
Maximum37.6
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:29.680377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile11.5
Q116.6
median19.5
Q323.1
95-th percentile28.2
Maximum37.6
Range36
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation4.9444561
Coefficient of variation (CV)0.25039947
Kurtosis-0.1761817
Mean19.746272
Median Absolute Deviation (MAD)3.2
Skewness0.0063978259
Sum2723800.8
Variance24.447646
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.403745977 × 10-29
2023-04-25T00:42:29.972645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 1513
 
1.0%
18.8 1492
 
1.0%
19.4 1476
 
1.0%
19.3 1449
 
1.0%
18.9 1447
 
1.0%
19.1 1444
 
1.0%
19 1428
 
1.0%
18.6 1415
 
1.0%
18.7 1390
 
1.0%
19.7 1390
 
1.0%
Other values (324) 123496
85.2%
(Missing) 7044
 
4.9%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
2.1 1
 
< 0.1%
2.5 2
 
< 0.1%
3.2 2
 
< 0.1%
3.4 1
 
< 0.1%
3.5 2
 
< 0.1%
3.6 1
 
< 0.1%
3.7 2
 
< 0.1%
3.8 5
< 0.1%
3.9 5
< 0.1%
ValueCountFrequency (%)
37.6 1
< 0.1%
36.8 1
< 0.1%
36.7 1
< 0.1%
36.5 1
< 0.1%
36.4 1
< 0.1%
36.3 1
< 0.1%
36.2 1
< 0.1%
35.8 2
< 0.1%
35.7 2
< 0.1%
35.6 1
< 0.1%
2023-04-25T00:42:31.318873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

TEMPERATURA DO PONTO DE ORVALHO(°C)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct264
Distinct (%)0.2%
Missing7273
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean14.297156
Minimum-6.3
Maximum25.6
Zeros3
Zeros (%)< 0.1%
Memory size1.1 MiB
2023-04-25T00:42:31.728910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-6.3
5-th percentile7.6
Q111.8
median14.9
Q317.3
95-th percentile19.1
Maximum25.6
Range31.9
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.6643479
Coefficient of variation (CV)0.25629909
Kurtosis0.01588615
Mean14.297156
Median Absolute Deviation (MAD)2.6
Skewness-0.67772688
Sum1968875.6
Variance13.427446
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.024320719 × 10-26
2023-04-25T00:42:32.011488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 1772
 
1.2%
17.8 1755
 
1.2%
17.1 1755
 
1.2%
17.6 1755
 
1.2%
17.4 1751
 
1.2%
17.9 1739
 
1.2%
17.5 1737
 
1.2%
17 1702
 
1.2%
17.3 1699
 
1.2%
17.2 1680
 
1.2%
Other values (254) 120366
83.0%
(Missing) 7273
 
5.0%
ValueCountFrequency (%)
-6.3 1
< 0.1%
-6.1 1
< 0.1%
-5.9 2
< 0.1%
-5.5 2
< 0.1%
-5.3 1
< 0.1%
-5.1 1
< 0.1%
-4.9 2
< 0.1%
-4.7 1
< 0.1%
-4.3 2
< 0.1%
-3.7 2
< 0.1%
ValueCountFrequency (%)
25.6 1
 
< 0.1%
24.6 1
 
< 0.1%
22.6 1
 
< 0.1%
22.5 2
 
< 0.1%
22.4 2
 
< 0.1%
22.2 2
 
< 0.1%
22.1 4
< 0.1%
22 3
< 0.1%
21.9 5
< 0.1%
21.8 5
< 0.1%
2023-04-25T00:42:33.413286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

TEMPERATURA MAXIMA NA HORA ANT. (AUT)(°C)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct338
Distinct (%)0.2%
Missing7458
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean20.483559
Minimum2.1
Maximum37.8
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:34.020256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile12
Q117.2
median20.1
Q324.1
95-th percentile29.2
Maximum37.8
Range35.7
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation5.1332478
Coefficient of variation (CV)0.25060332
Kurtosis-0.30479013
Mean20.483559
Median Absolute Deviation (MAD)3.4
Skewness0.040696688
Sum2817021.9
Variance26.350233
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.839087566 × 10-29
2023-04-25T00:42:34.551833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 1481
 
1.0%
19.5 1390
 
1.0%
19.1 1369
 
0.9%
19.3 1354
 
0.9%
19.4 1348
 
0.9%
19 1342
 
0.9%
18.9 1329
 
0.9%
19.9 1327
 
0.9%
18.8 1324
 
0.9%
19.7 1316
 
0.9%
Other values (328) 123946
85.5%
(Missing) 7458
 
5.1%
ValueCountFrequency (%)
2.1 1
 
< 0.1%
2.5 1
 
< 0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.8 2
< 0.1%
3.9 4
< 0.1%
4 1
 
< 0.1%
4.1 1
 
< 0.1%
4.3 1
 
< 0.1%
4.4 2
< 0.1%
ValueCountFrequency (%)
37.8 1
< 0.1%
37.6 1
< 0.1%
37.4 1
< 0.1%
37.2 1
< 0.1%
37 1
< 0.1%
36.9 1
< 0.1%
36.7 2
< 0.1%
36.6 1
< 0.1%
36.4 2
< 0.1%
36.3 1
< 0.1%
2023-04-25T00:42:37.087597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

TEMPERATURA MINIMA NA HORA ANT. (AUT)(°C)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct322
Distinct (%)0.2%
Missing7458
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean19.054438
Minimum1.4
Maximum36.2
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:37.778269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile10.9
Q116.1
median19
Q322.1
95-th percentile27.1
Maximum36.2
Range34.8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7717466
Coefficient of variation (CV)0.25042705
Kurtosis-0.086776639
Mean19.054438
Median Absolute Deviation (MAD)3
Skewness-0.050808926
Sum2620480.6
Variance22.769565
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.127694123 × 10-27
2023-04-25T00:42:38.346211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 1595
 
1.1%
18.7 1566
 
1.1%
19.1 1538
 
1.1%
19 1528
 
1.1%
18.8 1519
 
1.0%
18.9 1510
 
1.0%
18.6 1509
 
1.0%
18.4 1478
 
1.0%
19.4 1472
 
1.0%
19.3 1466
 
1.0%
Other values (312) 122345
84.4%
(Missing) 7458
 
5.1%
ValueCountFrequency (%)
1.4 2
< 0.1%
1.9 1
 
< 0.1%
2.5 4
< 0.1%
2.6 2
< 0.1%
2.9 1
 
< 0.1%
3.1 1
 
< 0.1%
3.2 2
< 0.1%
3.4 1
 
< 0.1%
3.5 4
< 0.1%
3.6 2
< 0.1%
ValueCountFrequency (%)
36.2 1
 
< 0.1%
35.4 3
< 0.1%
35 1
 
< 0.1%
34.8 1
 
< 0.1%
34.7 1
 
< 0.1%
34.6 2
< 0.1%
34.5 1
 
< 0.1%
34.3 4
< 0.1%
34.2 1
 
< 0.1%
33.9 1
 
< 0.1%
2023-04-25T00:42:40.201059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT)(°C)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct266
Distinct (%)0.2%
Missing7700
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean14.900299
Minimum-5.1
Maximum26.4
Zeros2
Zeros (%)< 0.1%
Memory size1.1 MiB
2023-04-25T00:42:40.608641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-5.1
5-th percentile8.3
Q112.4
median15.5
Q317.8
95-th percentile19.7
Maximum26.4
Range31.5
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation3.6126735
Coefficient of variation (CV)0.24245644
Kurtosis-0.15117382
Mean14.900299
Median Absolute Deviation (MAD)2.6
Skewness-0.61082779
Sum2045572.7
Variance13.05141
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.108640077 × 10-26
2023-04-25T00:42:40.884721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 1831
 
1.3%
17.7 1763
 
1.2%
18.2 1758
 
1.2%
17.6 1757
 
1.2%
18.4 1728
 
1.2%
17.9 1727
 
1.2%
17.5 1727
 
1.2%
18 1709
 
1.2%
18.5 1690
 
1.2%
17.8 1688
 
1.2%
Other values (256) 119906
82.7%
(Missing) 7700
 
5.3%
ValueCountFrequency (%)
-5.1 1
 
< 0.1%
-4.9 1
 
< 0.1%
-4.7 1
 
< 0.1%
-4.4 1
 
< 0.1%
-3.8 1
 
< 0.1%
-3.6 1
 
< 0.1%
-3.5 1
 
< 0.1%
-3.2 1
 
< 0.1%
-3 3
< 0.1%
-2.7 1
 
< 0.1%
ValueCountFrequency (%)
26.4 1
 
< 0.1%
24.7 1
 
< 0.1%
24.6 1
 
< 0.1%
24.4 1
 
< 0.1%
23.9 1
 
< 0.1%
23.5 2
< 0.1%
23.4 1
 
< 0.1%
23.3 1
 
< 0.1%
23.2 2
< 0.1%
23.1 3
< 0.1%
2023-04-25T00:42:42.180056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT)(°C)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct268
Distinct (%)0.2%
Missing7700
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean13.74131
Minimum-8.1
Maximum22
Zeros16
Zeros (%)< 0.1%
Memory size1.1 MiB
2023-04-25T00:42:42.574397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-8.1
5-th percentile6.9
Q111.2
median14.4
Q316.8
95-th percentile18.6
Maximum22
Range30.1
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation3.7491126
Coefficient of variation (CV)0.27283516
Kurtosis0.15362054
Mean13.74131
Median Absolute Deviation (MAD)2.7
Skewness-0.72483653
Sum1886462
Variance14.055845
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.535547105 × 10-27
2023-04-25T00:42:42.860917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.4 1753
 
1.2%
17 1719
 
1.2%
17.2 1704
 
1.2%
17.3 1683
 
1.2%
16.8 1676
 
1.2%
17.1 1674
 
1.2%
16.9 1671
 
1.2%
16.4 1654
 
1.1%
17.5 1651
 
1.1%
17.6 1650
 
1.1%
Other values (258) 120449
83.1%
(Missing) 7700
 
5.3%
ValueCountFrequency (%)
-8.1 1
< 0.1%
-8 1
< 0.1%
-7.5 1
< 0.1%
-7.3 1
< 0.1%
-6.8 1
< 0.1%
-6.6 1
< 0.1%
-6.4 1
< 0.1%
-6.3 2
< 0.1%
-5.9 1
< 0.1%
-5.6 2
< 0.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
21.7 1
 
< 0.1%
21.5 1
 
< 0.1%
21.3 1
 
< 0.1%
21.2 3
 
< 0.1%
21 1
 
< 0.1%
20.9 2
 
< 0.1%
20.8 3
 
< 0.1%
20.7 9
< 0.1%
20.6 18
< 0.1%
2023-04-25T00:42:44.177736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

UMIDADE REL. MAX. NA HORA ANT. (AUT)(%)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct89
Distinct (%)0.1%
Missing7698
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean77.468467
Minimum12
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:44.574017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile41
Q165
median84
Q393
95-th percentile97
Maximum100
Range88
Interquartile range (IQR)28

Descriptive statistics

Standard deviation18.469546
Coefficient of variation (CV)0.23841372
Kurtosis-0.11449871
Mean77.468467
Median Absolute Deviation (MAD)11
Skewness-0.92097544
Sum10635336
Variance341.12412
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-04-25T00:42:44.879327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 7548
 
5.2%
95 7128
 
4.9%
94 6380
 
4.4%
93 5715
 
3.9%
92 5533
 
3.8%
97 5410
 
3.7%
91 4662
 
3.2%
90 4143
 
2.9%
89 3837
 
2.6%
88 3572
 
2.5%
Other values (79) 83358
57.5%
(Missing) 7698
 
5.3%
ValueCountFrequency (%)
12 1
 
< 0.1%
13 3
 
< 0.1%
14 7
 
< 0.1%
15 4
 
< 0.1%
16 4
 
< 0.1%
17 19
 
< 0.1%
18 41
< 0.1%
19 59
< 0.1%
20 63
< 0.1%
21 85
0.1%
ValueCountFrequency (%)
100 763
 
0.5%
99 619
 
0.4%
98 2498
 
1.7%
97 5410
3.7%
96 7548
5.2%
95 7128
4.9%
94 6380
4.4%
93 5715
3.9%
92 5533
3.8%
91 4662
3.2%
2023-04-25T00:42:46.183541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

UMIDADE REL. MIN. NA HORA ANT. (AUT)(%)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct91
Distinct (%)0.1%
Missing7698
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean71.022879
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:46.572682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile33
Q155
median76
Q389
95-th percentile96
Maximum100
Range90
Interquartile range (IQR)34

Descriptive statistics

Standard deviation20.62704
Coefficient of variation (CV)0.2904281
Kurtosis-0.75623264
Mean71.022879
Median Absolute Deviation (MAD)15
Skewness-0.59923054
Sum9750447
Variance425.47477
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-04-25T00:42:46.875962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92 4189
 
2.9%
94 4052
 
2.8%
93 3969
 
2.7%
91 3940
 
2.7%
95 3860
 
2.7%
90 3797
 
2.6%
96 3628
 
2.5%
89 3595
 
2.5%
88 3448
 
2.4%
87 3320
 
2.3%
Other values (81) 99488
68.6%
(Missing) 7698
 
5.3%
ValueCountFrequency (%)
10 6
 
< 0.1%
11 4
 
< 0.1%
12 11
 
< 0.1%
13 16
 
< 0.1%
14 37
 
< 0.1%
15 62
< 0.1%
16 72
< 0.1%
17 117
0.1%
18 115
0.1%
19 143
0.1%
ValueCountFrequency (%)
100 358
 
0.2%
99 329
 
0.2%
98 1409
 
1.0%
97 2786
1.9%
96 3628
2.5%
95 3860
2.7%
94 4052
2.8%
93 3969
2.7%
92 4189
2.9%
91 3940
2.7%
2023-04-25T00:42:48.148243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

UMIDADE RELATIVA DO AR - HORARIA(%)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct90
Distinct (%)0.1%
Missing7272
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean74.271363
Minimum11
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:48.541117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile36
Q160
median80
Q391
95-th percentile96
Maximum100
Range89
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.685892
Coefficient of variation (CV)0.2650536
Kurtosis-0.48868045
Mean74.271363
Median Absolute Deviation (MAD)13
Skewness-0.7503954
Sum10228058
Variance387.53435
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-04-25T00:42:48.831138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 5485
 
3.8%
96 5443
 
3.8%
94 5201
 
3.6%
92 4925
 
3.4%
93 4890
 
3.4%
91 4314
 
3.0%
90 4017
 
2.8%
97 3966
 
2.7%
89 3728
 
2.6%
88 3577
 
2.5%
Other values (80) 92166
63.6%
(Missing) 7272
 
5.0%
ValueCountFrequency (%)
11 2
 
< 0.1%
12 5
 
< 0.1%
13 11
 
< 0.1%
14 9
 
< 0.1%
15 18
 
< 0.1%
16 34
 
< 0.1%
17 77
0.1%
18 75
0.1%
19 92
0.1%
20 107
0.1%
ValueCountFrequency (%)
100 552
 
0.4%
99 461
 
0.3%
98 1891
 
1.3%
97 3966
2.7%
96 5443
3.8%
95 5485
3.8%
94 5201
3.6%
93 4890
3.4%
92 4925
3.4%
91 4314
3.0%
2023-04-25T00:42:50.641486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF
Distinct360
Distinct (%)0.3%
Missing7430
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean151.10075
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:51.353070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22
Q186
median134
Q3192
95-th percentile336
Maximum360
Range359
Interquartile range (IQR)106

Descriptive statistics

Standard deviation93.894772
Coefficient of variation (CV)0.62140508
Kurtosis-0.44387604
Mean151.10075
Median Absolute Deviation (MAD)51
Skewness0.66407291
Sum20784512
Variance8816.2281
MonotonicityNot monotonic
2023-04-25T00:42:51.816139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 1374
 
0.9%
136 1352
 
0.9%
134 1315
 
0.9%
131 1308
 
0.9%
137 1295
 
0.9%
130 1289
 
0.9%
139 1289
 
0.9%
133 1281
 
0.9%
135 1279
 
0.9%
140 1279
 
0.9%
Other values (350) 124493
85.9%
(Missing) 7430
 
5.1%
ValueCountFrequency (%)
1 261
0.2%
2 289
0.2%
3 268
0.2%
4 279
0.2%
5 321
0.2%
6 268
0.2%
7 292
0.2%
8 269
0.2%
9 319
0.2%
10 289
0.2%
ValueCountFrequency (%)
360 255
0.2%
359 260
0.2%
358 286
0.2%
357 273
0.2%
356 269
0.2%
355 253
0.2%
354 265
0.2%
353 284
0.2%
352 285
0.2%
351 268
0.2%

VENTO - RAJADA MAXIMA(m/s)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct249
Distinct (%)0.2%
Missing7494
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean5.1829188
Minimum0
Maximum38.7
Zeros395
Zeros (%)0.3%
Memory size1.1 MiB
2023-04-25T00:42:53.057596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7
Q13
median4.8
Q36.8
95-th percentile10.1
Maximum38.7
Range38.7
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation2.7582668
Coefficient of variation (CV)0.53218407
Kurtosis2.6694125
Mean5.1829188
Median Absolute Deviation (MAD)1.8
Skewness1.1073434
Sum712599.5
Variance7.6080357
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-04-25T00:42:53.613795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 2247
 
1.5%
2.4 2195
 
1.5%
2.6 2194
 
1.5%
3.1 2183
 
1.5%
2.3 2169
 
1.5%
2.5 2166
 
1.5%
3 2164
 
1.5%
3.3 2148
 
1.5%
2.9 2115
 
1.5%
4.8 2052
 
1.4%
Other values (239) 115857
79.9%
(Missing) 7494
 
5.2%
ValueCountFrequency (%)
0 395
0.3%
0.1 11
 
< 0.1%
0.2 4
 
< 0.1%
0.3 6
 
< 0.1%
0.4 17
 
< 0.1%
0.5 31
 
< 0.1%
0.6 41
 
< 0.1%
0.7 87
 
0.1%
0.8 114
 
0.1%
0.9 175
0.1%
ValueCountFrequency (%)
38.7 1
 
< 0.1%
32.8 1
 
< 0.1%
32.5 1
 
< 0.1%
31.7 1
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
29.3 1
 
< 0.1%
27.5 1
 
< 0.1%
27.4 1
 
< 0.1%
27.1 3
< 0.1%
2023-04-25T00:42:55.955921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

VENTO - VELOCIDADE HORARIA(m/s)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct132
Distinct (%)0.1%
Missing7430
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean2.4084054
Minimum0
Maximum15.5
Zeros3582
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-25T00:42:56.404096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q11.2
median2.1
Q33.3
95-th percentile5.3
Maximum15.5
Range15.5
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.5882368
Coefficient of variation (CV)0.65945576
Kurtosis1.3695723
Mean2.4084054
Median Absolute Deviation (MAD)1
Skewness0.94486088
Sum331285.8
Variance2.5224962
MonotonicityNot monotonic
2023-04-25T00:42:56.664712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 4019
 
2.8%
1.4 3991
 
2.8%
1.3 3969
 
2.7%
1.7 3900
 
2.7%
1.6 3897
 
2.7%
1.2 3863
 
2.7%
1.8 3825
 
2.6%
1.9 3725
 
2.6%
2 3694
 
2.5%
1.1 3616
 
2.5%
Other values (122) 99055
68.3%
(Missing) 7430
 
5.1%
ValueCountFrequency (%)
0 3582
2.5%
0.1 1861
1.3%
0.2 1777
1.2%
0.3 1951
1.3%
0.4 1931
1.3%
0.5 2141
1.5%
0.6 2246
1.5%
0.7 2539
1.8%
0.8 2702
1.9%
0.9 3138
2.2%
ValueCountFrequency (%)
15.5 1
 
< 0.1%
14.8 1
 
< 0.1%
14.6 1
 
< 0.1%
14.3 2
< 0.1%
13.3 1
 
< 0.1%
13.2 2
< 0.1%
12.8 1
 
< 0.1%
12.7 3
< 0.1%
12.5 3
< 0.1%
12.2 3
< 0.1%

Interactions

2023-04-25T00:42:07.342846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:26.609288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:31.998677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:36.773247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:43.557270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:48.774443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:53.555451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:00.486732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:05.088427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:10.148604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:17.139287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:21.859812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:27.077971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:33.903485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:38.612872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:43.135612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:50.366723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:55.224379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:00.282007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:07.627727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:27.000422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:32.268866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:37.020700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:44.005820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:49.030845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:53.803658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:00.753150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:05.347155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:10.415245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:17.399282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:22.123234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:27.463688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:34.158524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:38.851436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:43.405825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:50.612993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:55.480540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:00.588563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:07.908974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:27.446329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:32.516377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:37.423826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:44.395673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:49.288615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:54.087876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:01.008997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:05.604439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:10.684815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:17.654492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:22.393729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:27.785331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:34.404059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:39.102592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:43.659187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:50.864788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:55.744092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:00.994677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:08.178250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:27.841874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:32.748952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:37.641754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:44.629993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:49.561890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:54.311026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:01.242847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:05.838059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:11.065394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:17.885115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:22.626608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:28.112040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:34.640746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:39.348654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:43.953519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:51.093431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:56.005844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:01.385201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:08.465972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:28.093486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:33.002815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:37.938277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:44.883343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:49.836415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:54.618448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:01.469518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:06.095919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:11.405735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:18.133350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:22.902441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:28.480047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:34.877978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:39.581647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:44.781148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:51.343315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:56.274193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:01.771056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:08.710352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:28.336835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:33.244217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:38.273583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:45.136571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:50.072667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:54.949296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:01.681823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:06.332122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:11.725501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:18.383256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:23.137261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:28.767709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:35.113271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:39.802631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:45.061724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:51.604470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:56.527225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:02.161866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:08.958574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:28.579822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:33.474736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:38.651185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:45.381359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:50.320534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:55.312755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:01.903080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:06.566334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:12.099965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:18.619652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:23.378390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:29.137083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:35.357647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T00:40:59.664727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:04.583386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:09.358453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:16.322435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:21.330346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:26.493367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:33.202850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:38.085784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:42.605342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:49.620159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:54.658958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:59.688427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:06.790645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:12.710443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:31.608092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:36.514931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:43.134404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:48.512136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:40:53.307315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:00.079180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:04.833761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:09.882728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:16.777000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:21.587967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:26.769581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:33.571390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:38.360608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:42.871778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:50.064193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:54.955681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:41:59.975366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T00:42:07.065840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-25T00:42:56.924290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
timePRECIPITACAO TOTAL - HORARIO(mm)PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB)PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB)PRESSAO ATMOSFERICA MAX.NA HORA ANT. (AUT)(mB)PRESSAO ATMOSFERICA MIN. NA HORA ANT. (AUT)(mB)RADIACAO GLOBAL(Kj/m²)TEMPERATURA DO AR - BULBO SECO - HORARIA(°C)TEMPERATURA DO PONTO DE ORVALHO(°C)TEMPERATURA MAXIMA NA HORA ANT. (AUT)(°C)TEMPERATURA MINIMA NA HORA ANT. (AUT)(°C)TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT)(°C)TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT)(°C)UMIDADE REL. MAX. NA HORA ANT. (AUT)(%)UMIDADE REL. MIN. NA HORA ANT. (AUT)(%)UMIDADE RELATIVA DO AR - HORARIA(%)VENTO - DIRECAO HORARIA (gr)(° (gr))VENTO - RAJADA MAXIMA(m/s)VENTO - VELOCIDADE HORARIA(m/s)
time1.000-0.0040.0270.0160.0250.0240.0790.030-0.0310.0280.035-0.028-0.030-0.071-0.060-0.065-0.003-0.014-0.031
PRECIPITACAO TOTAL - HORARIO(mm)-0.0041.000-0.182-0.119-0.181-0.191-0.054-0.0600.257-0.051-0.0400.2420.2580.2910.2490.2930.0400.0680.011
PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB)0.027-0.1821.0000.9270.9950.996-0.041-0.444-0.570-0.437-0.491-0.586-0.5510.0270.0320.010-0.154-0.0590.004
PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB)0.016-0.1190.9271.0000.9090.916-0.255-0.722-0.558-0.712-0.752-0.606-0.5180.2930.3060.291-0.123-0.205-0.090
PRESSAO ATMOSFERICA MAX.NA HORA ANT. (AUT)(mB)0.025-0.1810.9950.9091.0000.996-0.012-0.413-0.574-0.409-0.464-0.583-0.5570.0010.005-0.019-0.151-0.0380.018
PRESSAO ATMOSFERICA MIN. NA HORA ANT. (AUT)(mB)0.024-0.1910.9960.9160.9961.000-0.026-0.426-0.575-0.423-0.476-0.587-0.5560.0110.018-0.007-0.153-0.0580.005
RADIACAO GLOBAL(Kj/m²)0.079-0.054-0.041-0.255-0.012-0.0261.0000.558-0.0280.5370.4720.059-0.093-0.459-0.524-0.553-0.0380.3360.231
TEMPERATURA DO AR - BULBO SECO - HORARIA(°C)0.030-0.060-0.444-0.722-0.413-0.4260.5581.0000.3440.9870.9790.4300.280-0.689-0.717-0.7230.0010.3960.231
TEMPERATURA DO PONTO DE ORVALHO(°C)-0.0310.257-0.570-0.558-0.574-0.575-0.0280.3441.0000.3090.3670.9790.9840.3060.2850.2950.039-0.033-0.059
TEMPERATURA MAXIMA NA HORA ANT. (AUT)(°C)0.028-0.051-0.437-0.712-0.409-0.4230.5370.9870.3091.0000.9770.3980.242-0.723-0.760-0.7440.0100.4130.246
TEMPERATURA MINIMA NA HORA ANT. (AUT)(°C)0.035-0.040-0.491-0.752-0.464-0.4760.4720.9790.3670.9771.0000.4450.313-0.682-0.684-0.6790.0140.4020.243
TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT)(°C)-0.0280.242-0.586-0.606-0.583-0.5870.0590.4300.9790.3980.4451.0000.9620.2300.1950.2000.0500.019-0.023
TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT)(°C)-0.0300.258-0.551-0.518-0.557-0.556-0.0930.2800.9840.2420.3130.9621.0000.3560.3510.3490.030-0.055-0.066
UMIDADE REL. MAX. NA HORA ANT. (AUT)(%)-0.0710.2910.0270.2930.0010.011-0.459-0.6890.306-0.723-0.6820.2300.3561.0000.9660.9680.049-0.483-0.346
UMIDADE REL. MIN. NA HORA ANT. (AUT)(%)-0.0600.2490.0320.3060.0050.018-0.524-0.7170.285-0.760-0.6840.1950.3510.9661.0000.9810.036-0.474-0.327
UMIDADE RELATIVA DO AR - HORARIA(%)-0.0650.2930.0100.291-0.019-0.007-0.553-0.7230.295-0.744-0.6790.2000.3490.9680.9811.0000.057-0.471-0.331
VENTO - DIRECAO HORARIA (gr)(° (gr))-0.0030.040-0.154-0.123-0.151-0.153-0.0380.0010.0390.0100.0140.0500.0300.0490.0360.0571.000-0.011-0.007
VENTO - RAJADA MAXIMA(m/s)-0.0140.068-0.059-0.205-0.038-0.0580.3360.396-0.0330.4130.4020.019-0.055-0.483-0.474-0.471-0.0111.0000.831
VENTO - VELOCIDADE HORARIA(m/s)-0.0310.0110.004-0.0900.0180.0050.2310.231-0.0590.2460.243-0.023-0.066-0.346-0.327-0.331-0.0070.8311.000

Missing values

2023-04-25T00:42:13.122462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-25T00:42:13.947861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-25T00:42:15.187381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

timePRECIPITACAO TOTAL - HORARIO(mm)PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB)PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB)PRESSAO ATMOSFERICA MAX.NA HORA ANT. (AUT)(mB)PRESSAO ATMOSFERICA MIN. NA HORA ANT. (AUT)(mB)RADIACAO GLOBAL(Kj/m²)TEMPERATURA DO AR - BULBO SECO - HORARIA(°C)TEMPERATURA DO PONTO DE ORVALHO(°C)TEMPERATURA MAXIMA NA HORA ANT. (AUT)(°C)TEMPERATURA MINIMA NA HORA ANT. (AUT)(°C)TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT)(°C)TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT)(°C)UMIDADE REL. MAX. NA HORA ANT. (AUT)(%)UMIDADE REL. MIN. NA HORA ANT. (AUT)(%)UMIDADE RELATIVA DO AR - HORARIA(%)VENTO - DIRECAO HORARIA (gr)(° (gr))VENTO - RAJADA MAXIMA(m/s)VENTO - VELOCIDADE HORARIA(m/s)
00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
33.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
44.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
55.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
66.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
77.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
88.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
timePRECIPITACAO TOTAL - HORARIO(mm)PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO - HORARIA(mB)PRESSAO ATMOSFERICA REDUZIDA NIVEL DO MAR - AUT(mB)PRESSAO ATMOSFERICA MAX.NA HORA ANT. (AUT)(mB)PRESSAO ATMOSFERICA MIN. NA HORA ANT. (AUT)(mB)RADIACAO GLOBAL(Kj/m²)TEMPERATURA DO AR - BULBO SECO - HORARIA(°C)TEMPERATURA DO PONTO DE ORVALHO(°C)TEMPERATURA MAXIMA NA HORA ANT. (AUT)(°C)TEMPERATURA MINIMA NA HORA ANT. (AUT)(°C)TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT)(°C)TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT)(°C)UMIDADE REL. MAX. NA HORA ANT. (AUT)(%)UMIDADE REL. MIN. NA HORA ANT. (AUT)(%)UMIDADE RELATIVA DO AR - HORARIA(%)VENTO - DIRECAO HORARIA (gr)(° (gr))VENTO - RAJADA MAXIMA(m/s)VENTO - VELOCIDADE HORARIA(m/s)
144974144974.00.0905.51015.378898905.9905.52447711.022.915.625.022.218.415.273.059.063.0181.05.92.3
144975144975.00.0905.01013.806875905.5905.03312926.025.516.826.222.918.315.170.054.059.0159.05.61.0
144976144976.00.0904.41012.636340905.0904.33058699.026.817.927.224.418.314.963.049.058.0229.04.70.8
144977144977.00.0903.51012.203540904.4903.51750753.025.315.827.925.118.915.160.048.056.0118.03.90.7
144978144978.00.0902.61010.128050903.5902.62176.028.115.128.925.317.914.161.042.045.0275.05.62.9
144979144979.00.0902.01009.343544902.6902.02783398.028.415.429.127.518.214.353.042.045.0259.06.93.4
144980144980.00.0902.11009.681761902.1901.81695221.027.814.328.826.416.413.349.041.044.0289.05.91.6
144981144981.00.0903.01012.575533903.0902.1585316.022.916.327.822.917.013.668.043.066.0165.012.47.8
144982144982.00.0904.51015.167489904.5903.067735.020.616.822.920.516.816.279.066.079.0147.013.25.5
144983144983.00.2905.71017.197526905.7904.5-3102.018.917.020.618.917.016.789.079.089.0138.010.25.0